Focused On-demand Libraries - Receptor.AI Collaboration


Explore the Potential with AI-Driven Innovation

This comprehensive focused library is produced on demand with state-of-the-art virtual screening and parameter assessment technology driven by Receptor.AI drug discovery platform. This approach outperforms traditional methods and provides higher-quality compounds with superior activity, selectivity and safety.


Our selection of compounds is from a large virtual library of over 60 billion molecules. The production and distribution of these compounds are managed by Reaxense.


Contained in the library are leading modulators, each labelled with 38 ADME-Tox and 32 physicochemical and drug-likeness qualities. In addition, each compound is illustrated with its optimal docking poses, affinity scores, and activity scores, giving a complete picture.


We utilise our cutting-edge, exclusive workflow to develop focused libraries.


 

Fig. 1. The screening workflow of Receptor.AI

Utilising molecular simulations, our approach thoroughly examines a wide array of proteins, tracking their conformational changes individually and within complexes. Ensemble virtual screening enables us to address conformational flexibility, revealing essential binding sites at functional regions and allosteric locations. Our rigorous analysis guarantees that no potential mechanism of action is overlooked, aiming to uncover new therapeutic targets and lead compounds across diverse biological functions.


Our library stands out due to several important features:


  • The Receptor.AI platform compiles comprehensive data on the target protein, encompassing previous experiments, literature, known ligands, structural details, and more, leading to a higher chance of selecting the most relevant compounds.

  • Advanced molecular simulations on the platform help pinpoint potential binding sites, making the compounds in our focused library ideal for finding allosteric inhibitors and targeting cryptic pockets.

  • Receptor.AI boasts over 50 tailor-made AI models, rigorously tested and proven in various drug discovery projects and research initiatives. They are crafted for efficacy, dependability, and precision, all of which are key in creating our focused libraries.

  • Beyond creating focused libraries, Receptor.AI offers comprehensive services and complete solutions throughout the preclinical drug discovery phase. Our success-based pricing model minimises risk and maximises the mutual benefits of the project's success.


PARTNER
Receptor.AI
 
UPACC
O75923

UPID:
DYSF_HUMAN

ALTERNATIVE NAMES:
Dystrophy-associated fer-1-like protein; Fer-1-like protein 1

ALTERNATIVE UPACC:
O75923; A0FK00; B1PZ70; B1PZ71; B1PZ72; B1PZ73; B1PZ74; B1PZ75; B1PZ76; B1PZ77; B1PZ78; B1PZ79; B1PZ80; B1PZ81; B3KQB9; O75696; Q09EX5; Q0H395; Q53QY3; Q53TD2; Q8TEL8; Q9UEN7

BACKGROUND:
The protein Dysferlin, also known as Dystrophy-associated fer-1-like protein or Fer-1-like protein 1, is crucial for the Ca(2+)-triggered synaptic vesicle fusion with the plasma membrane. It significantly contributes to the repair of the sarcolemma in skeletal muscle and cardiomyocytes, facilitating quick membrane repair after physical damage. The gene associated with Dysferlin is identified by the accession number O75923.

THERAPEUTIC SIGNIFICANCE:
Given Dysferlin's critical role in several muscular dystrophies, including Limb-girdle muscular dystrophy, autosomal recessive 2, Miyoshi muscular dystrophy 1, and Distal myopathy with anterior tibial onset, its study offers a promising avenue for the development of targeted therapies. The exploration of Dysferlin's mechanisms opens doors to potential therapeutic strategies for these muscle-wasting diseases.

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